Abstract | ||
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A brain-computer interface (BCI) is a communication system that allows users to act on their environment by using only brain-activity. This paper presents a novel design of the auditory oddball task based brain-computer interface (BCI) system. The subject is presented with a stimulus presentation paradigm in which low-probability auditory targets are mixed with high-probability ones. In the data analysis, we employ a novel algorithm based on multivariate empirical mode decomposition that is used to extract informative brain activity features through thirteen electrodes' recorded signal of each single electroencephalogram (EEG) trial. Comparing to the result of arithmetic mean of all trials, auditory topography of peak latencies of the evoked event-related potential (ERP) demonstrated that the proposed algorithm is efficient for the detection of P300 or P100 component of the ERP in the subject's EEG. As a result we have found new ways to process EEG signals to improve detection for a P100 and P300 based BCI system. |
Year | DOI | Venue |
---|---|---|
2010 | 10.1007/978-3-642-14932-0_18 | ICIC (2) |
Keywords | Field | DocType |
communication system,data analysis,electroencephalography,empirical mode decomposition,brain computer interface,arithmetic mean | Pattern recognition,Multivariate statistics,Computer science,Brain–computer interface,Oddball paradigm,Multivariate empirical mode decomposition,Communications system,Brain activity and meditation,Speech recognition,Artificial intelligence,Stimulus (physiology),Electroencephalography | Conference |
Volume | Issue | ISSN |
6216 LNAI | null | 0302-9743 |
ISBN | Citations | PageRank |
3-642-14931-6 | 0 | 0.34 |
References | Authors | |
7 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Qiwei Shi | 1 | 10 | 3.60 |
Wei Zhou | 2 | 0 | 0.34 |
Jianting Cao | 3 | 194 | 34.47 |
Danilo Mandic | 4 | 1641 | 173.32 |
T. Tanaka | 5 | 638 | 95.91 |
Tomasz M. Rutkowski | 6 | 302 | 58.37 |
Rubin Wang | 7 | 141 | 25.54 |